Abstract
We present a new method to detect the presence of the hollow heart, an internal disorder of the potato tubers, using hyperspectral imaging technology in the infrared region. A set of 468 hyperspectral cubes of images has been acquired from Agria variety potatoes, that have been cut later to check the presence of a hollow heart. We developed several experiments to recognize hollow heart potatoes using different Artificial Intelligence and Image Processing techniques. The results show that Support Vector Machines (SVM) achieve an accuracy of 89.1% of correct classification. This is an automatic and non-destructive approach, and it could be integrated into other machine vision developments.
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References
Potato World, World-wide potato production statistics. International Year of the Potato (2008), http://www.potato2008.org/en/world/index.html
Rex, B.L., Mazza, G.: Cause, control and detection of hollow heart in potatoes: A review. Am. J. Potato Res. 66(3) (1989)
Finney, E.E., Norris, K.H.: X-Ray scans for detecting hollow heart in potatoes. Am. J. Potato Res. 55(2) (1978)
Jivanuwong, S.: Nondestructive detection of hollow heart in potatoes using ultrasonics. Master Thesis. Virginia Polytechnic Institute (1998)
Elbatawi, I.E.: An acoustic impact method to detect hollow heart of potato tubers. Biosyst. Eng. 100, 206–213 (2008)
Sun, D.: Hyperspectral Imaging for Food Quality Analysis and Control. Academic Press Elsevier, San Diego (2009)
Singh, B.: Visible and near-infrared spectroscopic analysis of potatoes. M.Sc. Thesis, McGill University, Montreal, PQ, Canada (2005)
Al-Mallahi, A., Kataoka, T., Okamoto, H., Shibata, Y.: Detection of potato tubers using an ultraviolet imaging-based machine vision system. Biosyst. Eng. 105, 257–265 (2009)
Kang, S., Lee, K., Son, J.: On-line internal quality evaluation system for the processing potatoes. In: Food Process. Autom. Conf. Proc., Providence, Rhode Island (2008)
Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly Media, Sebastopol (2008)
Otsu, N.: A threshold selection method for gray level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979)
Dacal-Nieto, A., Formella, A., Carrión, P., Vazquez-Fernandez, E., Fernández-Delgado, M.: Common scab detection on potatoes using an infrared hyperspectral imaging system. In: Proceedings of ICIAP 2011. LNCS, Springer, Heidelberg (2011)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)
Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
García-López, F., García-Torres, M., Melián-Batista, B., Moreno-Pérez, J.A., Moreno-Vega, J.M.: Solving feature subset selection problem by a Parallel Scatter Search. Eur. J. Oper. Res. 169(2), 477–489 (2008)
Weihs, C.: Multivariate Exploratory Data Analysis and Graphics, A tutorial. J. Chemom. 7, 305–340 (1993)
Guetlein, M., Frank, E., Hall, M., Karwath, A.: Large Scale Attribute Selection Using Wrappers. In: Proc IEEE Symposium on Computational Intelligence and Data Mining, pp. 332–339 (2009)
Hall, M.: Correlation-based Feature Subset Selection for Machine Learning, Hamilton, New Zealand (1998)
Breiman, L.: Using Iterated Bagging to Debias Regressions. Mach. Learn. 45, 261–277 (2001)
Chang, C.C., Lin, C.J.: LIBSVM:a library for support vector machines (2008), http://www.csie.ntu.edu.tw/~cjlin/libsvm/
Le Cessie, S., Van Houwelingen, J.C.: Ridge Estimators in Logistic Regression. Appl. Stat. 41, 191–201 (1992)
Curcio, J.A., Petty, C.C.: The Near Infrared Absorption Spectrum of Liquid Water. J. Opt. Soc. Am. 41, 302–302 (1951)
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Dacal-Nieto, A., Formella, A., Carrión, P., Vazquez-Fernandez, E., Fernández-Delgado, M. (2011). Non–destructive Detection of Hollow Heart in Potatoes Using Hyperspectral Imaging. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23678-5_20
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DOI: https://doi.org/10.1007/978-3-642-23678-5_20
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